详细解释一下这段代码，每一句都要进行注解：for dataset in datasets: print(dataset) if dataset not in out_results: out_results[dataset] = {} for scene in data_dict[dataset]: print(scene) # Fail gently if the notebook has not been submitted and the test data is not populated. # You may want to run this on the training data in that case? img_dir = f'{src}/test/{dataset}/{scene}/images' if not os.path.exists(img_dir): continue # Wrap the meaty part in a try-except block. try: out_results[dataset][scene] = {} img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] print (f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' if not os.path.isdir(feature_dir): os.makedirs(feature_dir, exist_ok=True) t=time() index_pairs = get_image_pairs_shortlist(img_fnames, sim_th = 0.5644583, # should be strict min_pairs = 33, # we select at least min_pairs PER IMAGE with biggest similarity exhaustive_if_less = 20, device=device) t=time() -t timings['shortlisting'].append(t) print (f'{len(index_pairs)}, pairs to match, {t:.4f} sec') gc.collect() t=time() if LOCAL_FEATURE != 'LoFTR': detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True, device=device, resize_small_edge_to=600 ) gc.collect() t=time() -t timings['feature_detection'].append(t) print(f'Features detected in {t:.4f} sec') t=time() match_features(img_fnames, index_pairs, feature_dir=feature_dir,device=device) else: match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device, resize_to=(600, 800)) t=time() -t timings['feature_matching'].append(t) print(f'Features matched in {t:.4f} sec') database_path = f'{feature_dir}/colmap.db' if os.path.isfile(database_path): os.remove(database_path) gc.collect() import_into_colmap(img_dir, feature_dir=feature_dir,database_path=database_path) output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}' t=time() pycolmap.match_exhaustive(database_path) t=time() - t timings['RANSAC'].append(t) print(f'RANSAC in {t:.4f} sec')

这段代码是Python代码，用于图像匹配和三维重建。以下是每行代码的注释：

for dataset in datasets:  # 遍历数据集列表
    print(dataset)  # 输出当前数据集
    if dataset not in out_results:  # 如果当前数据集不在输出结果中
        out_results[dataset] = {}  # 添加一个空字典作为该数据集的输出结果
    for scene in data_dict[dataset]:  # 遍历当前数据集中的场景
        print(scene)  # 输出当前场景
        # 如果在测试集中，但该场景的图像数据未被提供，则跳过
        img_dir = f'{src}/test/{dataset}/{scene}/images'
        if not os.path.exists(img_dir):
            continue
        try:  # 尝试匹配图像并执行三维重建
            out_results[dataset][scene] = {}  # 添加一个空字典作为该场景的输出结果
            img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]]  # 获取当前场景中的图像文件名列表
            print(f"Got {len(img_fnames)} images")
            feature_dir = f'featureout/{dataset}{scene}'  # 设置特征输出目录
            if not os.path.isdir(feature_dir):  # 如果特征输出目录不存在，则创建该目录
                os.makedirs(feature_dir, exist_ok=True)
            t = time()
            # 获取图像对的候选列表
            index_pairs = get_image_pairs_shortlist(img_fnames, sim_th=0.5644583, min_pairs=33,
                                                     exhaustive_if_less=20, device=device)
            t = time() - t
            timings['shortlisting'].append(t)
            print(f'{len(index_pairs)}, pairs to match, {t:.4f} sec')
            gc.collect()  # 执行垃圾回收以释放内存
            t = time()
            # 如果不使用LoFTR，则检测图像中的特征点
            if LOCAL_FEATURE != 'LoFTR':
                detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True,
                                device=device, resize_small_edge_to=600)
                gc.collect()
                t = time() - t
                timings['feature_detection'].append(t)
                print(f'Features detected in {t:.4f} sec')
            # 匹配图像中的特征点
            match_features(img_fnames, index_pairs, feature_dir=feature_dir, device=device)
            # 如果使用LoFTR，则使用LoFTR进行特征匹配
            else:
                match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device,
                            resize_to=(600, 800))
            t = time() - t
            timings['feature_matching'].append(t)
            print(f'Features matched in {t:.4f} sec')
            database_path = f'{feature_dir}/colmap.db'
            if os.path.isfile(database_path):
                os.remove(database_path)
            gc.collect()
            # 将特征匹配结果导入到COLMAP数据库中
            import_into_colmap(img_dir, feature_dir=feature_dir, database_path=database_path)
            output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}'
            t = time()
            # 使用COLMAP执行RANSAC算法进行三维重建
            pycolmap.match_exhaustive(database_path)
            t = time() - t
            timings['RANSAC'].append(t)
            print(f'RANSAC in {t:.4f} sec')
        except Exception as e:  # 捕获任何异常
            print(f'Scene {scene} failed. Error: {e}')

此代码的主要目的是使用图像匹配和三维重建技术来重建场景。在此过程中，它使用了许多库和函数，例如os、time、gc、pycolmap等。